SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 5551–5575 of 10307 papers

TitleStatusHype
Quality versus Quantity: Building Catalan-English MT Resources—0
Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements—0
Optimization with Access to Auxiliary InformationCode0
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference—0
Multi-task Optimization Based Co-training for Electricity Consumption Prediction—0
Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals—0
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling—0
VFed-SSD: Towards Practical Vertical Federated Advertising—0
Variational Transfer Learning using Cross-Domain Latent Modulation—0
Underwater Acoustic Communication Channel Modeling using Reservoir Computing—0
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA—0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
Long-Tailed Learning Requires Feature Learning—0
Parameter-Efficient and Student-Friendly Knowledge Distillation—0
Looks Like Magic: Transfer Learning in GANs to Generate New Card Illustrations—0
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning—0
Transfer Learning-based Channel Estimation in Orthogonal Frequency Division Multiplexing Systems Using Data-nulling Superimposed PilotsCode0
Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations—0
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning—0
Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation—0
Transfer learning driven design optimization for inertial confinement fusion—0
Balancing Data through Data Augmentation Improves the Generality of Transfer Learning for Diabetic Retinopathy Classification—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified